3
University of Sheffield, NLP The easiest way to learn… … is to get your hands dirty!

4
University of Sheffield, NLP Before you start annotating... You need to think about annotation guidelines You need to consider what you want to annotate and then to define it appropriately With multiple annotators it's essential to have a clear set of guidelines for them to follow Consistency of annotation is really important for a proper evaluation

5
University of Sheffield, NLP Annotation Guidelines People need clear definition of what to annotate in the documents, with examples Typically written as a guidelines document Piloted first with few annotators, improved, then “real” annotation starts, when all annotators are trained Annotation tools may require the definition of a formal DTD (e.g. XML schema) –What annotation types are allowed –What are their attributes/features and their values –Optional vs obligatory; default values

8
University of Sheffield, NLP Annotation Hands-On Exercise‏ Create Key annotation set –Type Key in the bottom of annotation set view and press the New button Select it in the annotation set view Annotate all instances of “Sheffield” with Location annotations in the Key set Save the resulting document as xml

12
University of Sheffield, NLP Annotation Schemas Hands-on-Exercise Unload the ANNIE plugin Change the AddressSchema.xml to add “city” as another possible value for the feature “kind” (see evaluation- materials/AddressSchema.xml) Look at the evaluation-materials/creole.xml and see how it automatically instantiates the schema Load the the evaluation-materials directory as a plugin Load the Schema Annotation Editor plugins/Schema_Annotation_Editor Load the Sheffield.xml document evaluation-materials/Sheffield.xml Annotate all occurrences of “Sheffield” as Address with feature kind = city

13
University of Sheffield, NLP Coreference annotation Different expressions refer to the same entity – e.g. UK, United Kingdom – e.g. Prof. Cunningham, Hamish Cunningham, H. Cunningham, Cunningham, H. Orthomatcher PR – co-reference resolution based on orthographical information of entities – Produces a list of annotation ids that form a co-reference chain – List of such lists stored as a document feature named “matches”

14
University of Sheffield, NLP Coreference annotation DEMO

15
University of Sheffield, NLP Coreference annotation Hands-on-Exercise Load the Sheffield.xml document in a corpus and run ANNIE without Orthomatcher Open document and go to the Co-reference Editor See what chains are created? Highlight the chain with string “Sheffield Hallam University” Remove the invalid members. Highlight annotations of type “Organization” Create a new chain for “Sheffield University” and assign other mentions of the same to the same chain

16
University of Sheffield, NLP Ontology-based Annotation This will be covered in the lecture on Ontologies (Wed afternoon)‏ Uses a similar approach to the regular annotation GUI We can practise more annotation in the ad-hoc sessions for non-programmers – please ask if interested

17
University of Sheffield, NLP “ We didn’t underperform. You overexpected.” Evaluation

18
University of Sheffield, NLP Performance Evaluation 2 main requirements: Evaluation metric: mathematically defines how to measure the system’s performance against human-annotated gold standard Scoring program: implements the metric and provides performance measures –For each document and over the entire corpus –For each type of annotation

19
University of Sheffield, NLP Evaluation Metrics Most common are Precision and Recall Precision = correct answers/answers produced (what proportion of the answers produced are accurate?) Recall = correct answers/total possible correct answers (what proportion of all the correct answers did the system find?)‏ Trade-off between Precision and Recall F1 (balanced) Measure = 2PR / 2(R + P) Some tasks sometimes use other metrics, e.g. cost- based (good for application-specific adjustment)‏

20
University of Sheffield, NLP AnnotationDiff Graphical comparison of 2 sets of annotations Visual diff representation, like tkdiff Compares one document at a time, one annotation type at a time Gives scores for precision, recall, F- measure etc. Traditionally, partial matches (mismatched spans) are given a half weight Strict considers them as incorrect; lenient considers them as correct

21
University of Sheffield, NLP Annotations are like squirrels… Annotation Diff helps with “spot the difference”

24
University of Sheffield, NLP Corpus Benchmark Tool Compares annotations at the corpus level Compares all annotation types at the same time, i.e. gives an overall score, as well as a score for each annotation type Enables regression testing, i.e. comparison of 2 different versions against gold standard Visual display, can be exported to HTML Granularity of results: user can decide how much information to display Results in terms of Precision, Recall, F-measure

25
University of Sheffield, NLP Corpus structure Corpus benchmark tool requires a particular directory structure Each corpus must have a clean and marked sub-directory Clean holds the unannotated version, while marked holds the marked (gold standard) ones There may also be a processed subdirectory – this is a datastore (unlike the other two)‏ Corresponding files in each subdirectory must have the same name

26
University of Sheffield, NLP How it works Clean, marked, and processed Corpus_tool.properties – must be in the directory where build.xml is Specifies configuration information about –What annotation types are to be evaluated –Threshold below which to print out debug info –Input set name and key set name Modes –Storing results for later use –Human marked against already stored, processed –Human marked against current processing results –Regression testing – default mode

29
University of Sheffield, NLP Ontology-based evaluation: BDM Traditional methods for IE (Precision and Recall) are not sufficient for ontology-based IE The distinction between right and wrong is less obvious Recognising a Person as a Location is clearly wrong, but recognising a Research Assistant as a Lecturer is not so wrong Integration of similarity metrics enable closely related items some credit

30
University of Sheffield, NLP Which things are most similar?

31
University of Sheffield, NLP Balanced Distance Metric Considers the relative specificity of the taxonomic positions of the key and response Unlike some algorithms, does not distinguish between the directionality of this relative specificity, Distances are normalised wrt average length of chain Makes the penalty in terms of node traversal relative to the semantic density of the concepts in question More information in the LREC 2008 paper “Evaluating Evaluation Metrics for Ontology-Based Applications” available from the GATE website

33
University of Sheffield, NLP BDM Plugin Load the BDMComputation Plugin, load a BDMComputation PR and add it to a corpus pipeline Set the parameters – ontologyURL (location of the ontology)‏ – outputBDMFile (plain text file to store the BDM values)‏ Result will be written to this file with BDM scores for each match This file can be used as input for the IAA plugin

34
University of Sheffield, NLP IAA Plugin This computes inter-annotator agreement. Uses the same computation as the corpus benchmarking tool but can compare more than 2 sets simultaneously Also enables calculation using BDM Can be used for classification tasks also to compute Kappa and other measures Load the IAA Plugin and then an IAA Computation PR, and add it to a pipeline. If using the BDM, select the BDM results file

35
University of Sheffield, NLP More on using the evaluation plugins More detail and hands-on practice with the evaluation plugins during the ad-hoc sessions for non-programmers Please ask if interested